ICML 2016 is in New York City. I expect it to be the largest ICML by far given the destination—New York is the place which is perhaps easiest to reach from anywhere in the world and it has the largest machine learning meetup anywhere in the world.
I am the general chair this year, which is light in work but heavy in responsibilities. Some things I worry about:
- How many people will actually come? Numbers are difficult to guess with the field growing and the conference changing locations. I believe we need capacity for at least 3000 people based on everything I know.
- New York is expensive. What can be done about it? One thought is that we should actively setup a roommate finding system so the costs of hotels can be shared. Up to 3 people can share a hotel room for the conference hotel (yes, each with their own bed), and that makes the price much more reasonable. I’m also hoping donations will substantially defray the cost. If others have creative ideas, I’m definitely interested.
Markus Weimer also points out the 2016 KDD Cup which has a submission deadline of December 6. KDD Cup datasets have become common reference for many machine learning papers, so this is a good way to get your problem solved well by many people.
I just created Vowpal Wabbit 7.8, and we are planning to have an increasingly less heretical followup tutorial during the non-“ski break” at the NIPS Optimization workshop. Please join us if interested.
I always feel like things are going slow, but in the last year, but there have been many changes overall. Notes for 7.7 are here. Since then, there are several areas of improvement as well as generalized bug fixes and refactoring.
- Learning to Search: Hal completely rewrote the learning to search system, enough that the numbers here are looking obsolete. Kai-Wei has also created several advanced applications for entity-relation and dependency parsing which are promising.
- Languages Hal also created a good python library, which includes call-backs for learning to search. You can now develop advanced structured prediction solutions in a nice language. Jonathan Morra also contributed an initial Java interface.
- Exploration The contextual bandit subsystem now allows evaluation of an arbitrary policy, and an exploration library is now factored out into an independent library (principally by Luong with help from Sid and Sarah). This is critical for real applications because randomization must happen at the point of decision.
- Reductions The learning reductions subsystem has continued to mature, although the perfectionist in me is still dissatisfied. As a consequence, it’s now pretty easy to program new reductions, and the efficiency of these reductions has generally improved. Several news ones are cooking.
- Online Learning Alekh added an online SVM implementation based on LaSVM. This is known to parallelize well via the para-active approach.
This project has grown quite a bit—there are about 30 different people contributing to VW since the last release, and there is now a VW meetup (December 8th!) in the bay area that I wish I could attend.
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On August 22, we are planning to have an Open Machine Learning Workshop at MSR, New York City taking advantage of CJ Lin and others in town for KDD.
If you are interested, please email msrnycrsvp at microsoft.com and say “I want to come” so we can get a count of attendees for refreshments.
Added: Videos are now online.
I’d like to propose that ICML conducts a yearly survey similar to the one from 2010 or 2012 which is reported to all.
The key reason for this is information: I expect everyone participating in ICML has some baseline interest in how ICML is doing. Everyone involved has personal anecdotal information, but we all understand that a few examples can be highly misleading.
Aside from satisfying everyone’s joint curiousity, I believe this could improve ICML itself. Consider for example reviewing. Every program chair comes in with ideas for how to make reviewing better. Some succeed, but nearly all are forgotten by the next round of program chairs. Making survey information available will help quantify success and correlate it with design decisions.
The key question to ask for this is “who?” The reason why surveys don’t happen more often is that it has been the responsibility of program chairs who are typically badly overloaded. I believe we should address this by shifting the responsibility to a multiyear position, similar to or the same as a webmaster. This may imply a small cost to the community (<$1/participant) for someone’s time to do and record the survey, but I believe it’s a worthwhile cost.
I plan to bring this up with IMLS board in Beijing, but would like to invite any comments or thoughts.
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1314 is New York Machine Learning Symposium is finally happening on March 28th at the New York Academy of Science. Every invited speaker interests me personally. They are:
We’ve been somewhat disorganized in advertising this. As a consequence, anyone who has not submitted an abstract but would like to do so may send one directly to me (firstname.lastname@example.org title NYASMLS) by Friday March 14. I will forward them to the rest of the committee for consideration.